Prior research on such tasks has often relied on
modifications to the representations of these elements,
via careful input coding or appropriate pre-training,
that introduce shared features as a means
to achieve generalisation to the task test sets
[1]}, [2]}, [3]}.
In fact, it has commonly been asserted that
generalisation in neural networks requires such featural overlap
[4]}, [5]}, [6]}
and this characteristic is frequently invoked as a benefit of
distributed representations [7]}, [8]}.
Here, we show that these models are capable of
generalising to test data that lacks any features in common
with their training data by exploiting symmetries
relevant to the task.
Architecturally, this is achieved using convolutional
[9]}, [10]}
layers, a common component in deep learning approaches
to image categorisation,
and our solutions repurpose their spatial symmetries
to serve the needs of each problem.
